Electrophysiological Biomarkers to Optimize DBS for Depression

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PROJECT SUMMARY Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) white matter is an emerging new treatment strategy for treatment resistant depression (TRD) with published studies demonstrating sustained long-term antidepressant effects in 40-60% of implanted patients. Converging evidence from positron emission tomography (PET), electroencephalography (EEG) and diffusion tractography (DTI) strongly suggests that DBS mediates its clinical benefits by direct modulation of the SCC--a key hub in an aberrant neural circuit. Despite encouraging sustained long-term effects in this notoriously difficult to treat patient population, randomized controls trials of SCC DBS and other DBS targets for TRD are now on hold as initial results failed to meet predefined clinical endpoints. While this proposal cannot address those failures directly, a clear necessary next step for effective future testing and eventual dissemination of this treatment is the need to develop brain-based biomarkers to guide lead placement and to titrate stimulation parameters during ongoing care. In the absence of such biomarkers to guide DBS use, there will continue to be variability in the implementation of clinical procedures during testing, leading to ambiguous and possibly misleading trial outcomes, and subsequent abandonment of a potentially useful treatment. To overcome these limitations, we propose to develop and test objective methods for reliable device configuration in individuals by optimizing DBS-SCC treatment with respect to human functional anatomy and key electrophysiological variables. We will leverage the capabilities of a novel bi-directional neuromodulation system (Medtronic RC+S) that allows live streaming of oscillatory activity at the site of stimulation to define novel control strategies to guide programming decisions for DBS delivery. Ongoing measurements of SCC local field potentials (LFPs) will be combined with electroencephalography (EEG) and event related potential studies (ERP) performed as part of an experimental clinical trial of subcallosal cingulate DBS for TRD to identify an oscillatory signal that (1) is sensitive to changes in frequency and current parameters at the tractography defined optimal target and (2) tracks with depression state over time. Connectome-based and machine learning approaches will be used to define the most robust network biomarker and its response characteristics. Once defined, the control policy will be tested in a second phase feasibility study where parameters for initial stimulation will be selected based on the depression brain state biomarker and adjustments made to correct drift from the predefined target signal. If successful, the data- driven model and control strategy will enable objective, rational clinical programming of DBS stimulation for depression and provide a new model and approach for target identification, stimulation initiation and long-term monitoring and management of patients receiving this treatment. .